
"""simple docstring"""


from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


SCREAMING_SNAKE_CASE			  =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			  =  {
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}



class 	__a	(   snake_case_						):
       UpperCamelCase_		:       int				=      'marian'
       UpperCamelCase_		:       Optional[int]				=      ['past_key_values']
       UpperCamelCase_		:       Optional[int]				=      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}






       def __init__(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				Dict=58_101							,   UpperCAmelCase_			:				int=None							,   UpperCAmelCase_			:				List[str]=1_024							,   UpperCAmelCase_			:				Union[str, Any]=12							,   UpperCAmelCase_			:				Dict=4_096							,   UpperCAmelCase_			:				List[Any]=16							,   UpperCAmelCase_			:				Union[str, Any]=12							,   UpperCAmelCase_			:				Union[str, Any]=4_096							,   UpperCAmelCase_			:				Optional[Any]=16							,   UpperCAmelCase_			:				Tuple=0.0							,   UpperCAmelCase_			:				Tuple=0.0							,   UpperCAmelCase_			:				List[Any]=True							,   UpperCAmelCase_			:				List[str]=True							,   UpperCAmelCase_			:				Union[str, Any]="gelu"							,   UpperCAmelCase_			:				Any=1_024							,   UpperCAmelCase_			:				Dict=0.1							,   UpperCAmelCase_			:				Optional[Any]=0.0							,   UpperCAmelCase_			:				str=0.0							,   UpperCAmelCase_			:				int=0.02							,   UpperCAmelCase_			:				Union[str, Any]=58_100							,   UpperCAmelCase_			:				str=False							,   UpperCAmelCase_			:				int=58_100							,   UpperCAmelCase_			:				Union[str, Any]=0							,   UpperCAmelCase_			:				Dict=0							,   UpperCAmelCase_			:				str=True							,   **UpperCAmelCase_			:				List[Any]							,   )->	Union[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		vocab_size
              UpperCamelCase       			=		decoder_vocab_size or vocab_size
              UpperCamelCase       			=		max_position_embeddings
              UpperCamelCase       			=		d_model
              UpperCamelCase       			=		encoder_ffn_dim
              UpperCamelCase       			=		encoder_layers
              UpperCamelCase       			=		encoder_attention_heads
              UpperCamelCase       			=		decoder_ffn_dim
              UpperCamelCase       			=		decoder_layers
              UpperCamelCase       			=		decoder_attention_heads
              UpperCamelCase       			=		dropout
              UpperCamelCase       			=		attention_dropout
              UpperCamelCase       			=		activation_dropout
              UpperCamelCase       			=		activation_function
              UpperCamelCase       			=		init_std
              UpperCamelCase       			=		encoder_layerdrop
              UpperCamelCase       			=		decoder_layerdrop
              UpperCamelCase       			=		use_cache
              UpperCamelCase       			=		encoder_layers
              UpperCamelCase       			=		scale_embedding  # scale factor will be sqrt(d_model) if True
              UpperCamelCase       			=		share_encoder_decoder_embeddings
              super().__init__(
                  pad_token_id=lowerCAmelCase__							,   eos_token_id=lowerCAmelCase__							,   is_encoder_decoder=lowerCAmelCase__							,   decoder_start_token_id=lowerCAmelCase__							,   forced_eos_token_id=lowerCAmelCase__							,   **lowerCAmelCase__							,   )





class 	__a	(   snake_case_						):






       @property
       # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	Optional[Any]:







              """simple docstring"""
              if self.task in ["default", "seq2seq-lm"]:
                     UpperCamelCase       			=		OrderedDict(
                         [
                             ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                             ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                         ]      )

                     if self.use_past:
                            UpperCamelCase       			=		{0: '''batch'''}
                            UpperCamelCase       			=		{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
                     else:
                            UpperCamelCase       			=		{0: '''batch''', 1: '''decoder_sequence'''}
                            UpperCamelCase       			=		{0: '''batch''', 1: '''decoder_sequence'''}

                     if self.use_past:
                            self.fill_with_past_key_values_(lowerCAmelCase__							,   direction="inputs"      )
              elif self.task == "causal-lm":
                     # TODO: figure this case out.
                     UpperCamelCase       			=		OrderedDict(
                         [
                             ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                             ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                         ]      )
                     if self.use_past:
                            UpperCamelCase       			=		self.num_layers
                            for i in range(lowerCAmelCase__      ):
                                   UpperCamelCase       			=		{0: '''batch''', 2: '''past_sequence + sequence'''}
                                   UpperCamelCase       			=		{0: '''batch''', 2: '''past_sequence + sequence'''}
              else:
                     UpperCamelCase       			=		OrderedDict(
                         [
                             ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                             ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                             ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                             ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
                         ]      )

              return common_inputs






       @property
       # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	List[str]:







              """simple docstring"""
              if self.task in ["default", "seq2seq-lm"]:
                     UpperCamelCase       			=		super().outputs
              else:
                     UpperCamelCase       			=		super(lowerCAmelCase__							,   self      ).outputs
                     if self.use_past:
                            UpperCamelCase       			=		self.num_layers
                            for i in range(lowerCAmelCase__      ):
                                   UpperCamelCase       			=		{0: '''batch''', 2: '''past_sequence + sequence'''}
                                   UpperCamelCase       			=		{0: '''batch''', 2: '''past_sequence + sequence'''}
              return common_outputs






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple							,   UpperCAmelCase_			:				PreTrainedTokenizer							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				Optional[TensorType] = None							,   )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		self._generate_dummy_inputs_for_encoder_and_decoder(
                  lowerCAmelCase__							,   lowerCAmelCase__							,   lowerCAmelCase__							,   lowerCAmelCase__							,   lowerCAmelCase__      )

              # Generate decoder inputs
              UpperCamelCase       			=		seq_length if not self.use_past else 1
              UpperCamelCase       			=		self._generate_dummy_inputs_for_encoder_and_decoder(
                  lowerCAmelCase__							,   lowerCAmelCase__							,   lowerCAmelCase__							,   lowerCAmelCase__							,   lowerCAmelCase__      )
              UpperCamelCase       			=		{f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
              UpperCamelCase       			=		dict(**lowerCAmelCase__							,   **lowerCAmelCase__      )

              if self.use_past:
                     if not is_torch_available():
                            raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."      )
                     else:
                            import torch
                     UpperCamelCase       			=		common_inputs['''input_ids'''].shape
                     UpperCamelCase       			=		common_inputs['''decoder_input_ids'''].shape[1]
                     UpperCamelCase       			=		self.num_attention_heads
                     UpperCamelCase       			=		(
                         batch,
                         num_encoder_attention_heads,
                         encoder_seq_length,
                         self._config.hidden_size // num_encoder_attention_heads,
                     )
                     UpperCamelCase       			=		decoder_seq_length + 3
                     UpperCamelCase       			=		(
                         batch,
                         num_decoder_attention_heads,
                         decoder_past_length,
                         self._config.hidden_size // num_decoder_attention_heads,
                     )

                     UpperCamelCase       			=		torch.cat(
                         [common_inputs["decoder_attention_mask"], torch.ones(lowerCAmelCase__							,   lowerCAmelCase__      )]							,   dim=1      )

                     UpperCamelCase       			=		[]
                     # If the number of encoder and decoder layers are present in the model configuration, both are considered
                     UpperCamelCase       			=		self.num_layers
                     UpperCamelCase       			=		min(lowerCAmelCase__							,   lowerCAmelCase__      )
                     UpperCamelCase       			=		max(lowerCAmelCase__							,   lowerCAmelCase__      ) - min_num_layers
                     UpperCamelCase       			=		'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

                     for _ in range(lowerCAmelCase__      ):
                            common_inputs["past_key_values"].append(
                                (
                                    torch.zeros(lowerCAmelCase__      ),
                                    torch.zeros(lowerCAmelCase__      ),
                                    torch.zeros(lowerCAmelCase__      ),
                                    torch.zeros(lowerCAmelCase__      ),
                                )      )
                     # TODO: test this.
                     UpperCamelCase       			=		encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
                     for _ in range(lowerCAmelCase__							,   lowerCAmelCase__      ):
                            common_inputs["past_key_values"].append((torch.zeros(lowerCAmelCase__      ), torch.zeros(lowerCAmelCase__      ))      )
              return common_inputs






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]							,   UpperCAmelCase_			:				PreTrainedTokenizer							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				Optional[TensorType] = None							,   )->	Dict:







              """simple docstring"""
              UpperCamelCase       			=		self._generate_dummy_inputs_for_encoder_and_decoder(
                  lowerCAmelCase__							,   lowerCAmelCase__							,   lowerCAmelCase__							,   lowerCAmelCase__							,   lowerCAmelCase__      )

              if self.use_past:
                     if not is_torch_available():
                            raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."      )
                     else:
                            import torch
                     UpperCamelCase       			=		common_inputs['''input_ids'''].shape
                     # Not using the same length for past_key_values
                     UpperCamelCase       			=		seqlen + 2
                     UpperCamelCase       			=		self.num_layers
                     UpperCamelCase       			=		self.num_attention_heads
                     UpperCamelCase       			=		(
                         batch,
                         num_encoder_attention_heads,
                         past_key_values_length,
                         self._config.hidden_size // num_encoder_attention_heads,
                     )

                     UpperCamelCase       			=		common_inputs['''attention_mask'''].dtype
                     UpperCamelCase       			=		torch.cat(
                         [common_inputs["attention_mask"], torch.ones(lowerCAmelCase__							,   lowerCAmelCase__							,   dtype=lowerCAmelCase__      )]							,   dim=1      )
                     UpperCamelCase       			=		[
                         (torch.zeros(lowerCAmelCase__      ), torch.zeros(lowerCAmelCase__      )) for _ in range(lowerCAmelCase__      )
                     ]
              return common_inputs






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				PreTrainedTokenizer							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				Optional[TensorType] = None							,   )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		compute_effective_axis_dimension(
                  lowerCAmelCase__							,   fixed_dimension=OnnxConfig.default_fixed_batch							,   num_token_to_add=0      )

              # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
              UpperCamelCase       			=		tokenizer.num_special_tokens_to_add(lowerCAmelCase__      )
              UpperCamelCase       			=		compute_effective_axis_dimension(
                  lowerCAmelCase__							,   fixed_dimension=OnnxConfig.default_fixed_sequence							,   num_token_to_add=lowerCAmelCase__      )

              # Generate dummy inputs according to compute batch and sequence
              UpperCamelCase       			=		[''' '''.join([tokenizer.unk_token]      ) * seq_length] * batch_size
              UpperCamelCase       			=		dict(tokenizer(lowerCAmelCase__							,   return_tensors=lowerCAmelCase__      )      )
              return common_inputs






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict							,   UpperCAmelCase_			:				PreTrainedTokenizer							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				Optional[TensorType] = None							,   )->	Optional[int]:







              """simple docstring"""
              if self.task in ["default", "seq2seq-lm"]:
                     UpperCamelCase       			=		self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                         lowerCAmelCase__							,   batch_size=lowerCAmelCase__							,   seq_length=lowerCAmelCase__							,   is_pair=lowerCAmelCase__							,   framework=lowerCAmelCase__      )

              else:
                     UpperCamelCase       			=		self._generate_dummy_inputs_for_causal_lm(
                         lowerCAmelCase__							,   batch_size=lowerCAmelCase__							,   seq_length=lowerCAmelCase__							,   is_pair=lowerCAmelCase__							,   framework=lowerCAmelCase__      )

              return common_inputs






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				List[Any]							,   UpperCAmelCase_			:				Any							,   UpperCAmelCase_			:				Union[str, Any]      )->	Tuple:







              """simple docstring"""
              if self.task in ["default", "seq2seq-lm"]:
                     UpperCamelCase       			=		super()._flatten_past_key_values_(lowerCAmelCase__							,   lowerCAmelCase__							,   lowerCAmelCase__							,   lowerCAmelCase__      )
              else:
                     UpperCamelCase       			=		super(lowerCAmelCase__							,   self      )._flatten_past_key_values_(
                         lowerCAmelCase__							,   lowerCAmelCase__							,   lowerCAmelCase__							,   lowerCAmelCase__      )






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	Tuple:







              """simple docstring"""
              return 1e-4










'''simple docstring'''



def        UpperCAmelCase (    A	:					int       ,		A	:					int					):
							return x if y == 0 else greatest_common_divisor(A       ,		x % y					)
def        UpperCAmelCase (    A	:					int       ,		A	:					int					):
							return (x * y) // greatest_common_divisor(A       ,		A					)
def        UpperCAmelCase (    A	:					int = 20					):
							SCREAMING_SNAKE_CASE       :   Tuple         =      1
							for i in range(1       ,		n + 1					):
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      lcm(A       ,		A					)
							return g


if __name__ == "__main__":
							print(f'{solution() = }')




